Controlled source electromagnetic (CSEM) surveys for mapping subsurface resistivity (Srnka, 2003) have recently come into common use for hydrocarbon exploration. FIG. 1 illustrates a common implementation of CSEM surveying in a marine environment; a different implementation is used for land surveys, but the physical principles involved are the same. A slowly moving (typically 1-2 knots) boat 101 tows a dipole antenna 102 typically 100-300 m in length just above the seafloor 105, driven by a low frequency (typically <10 Hz) high-powered (e.g. 120 kW) source of electric current, which may be located in the boat. The source produces an electromagnetic field that diffuses through the seawater and into the earth. The precise pattern of this diffusion depends on how resistivity is distributed in the subsurface. Electric and magnetic fields recorded on a set of receivers 103 placed at fixed locations on the seafloor characterize this pattern and can therefore be used to infer the spatial distribution of subsurface resistivity, for example resistive region 104 located among background layers that themselves can have varying resistivity as indicated by different gray shadings.
CSEM surveys are useful for detecting hydrocarbon reservoirs because hydrocarbon-bearing porous rock is more resistive than the same rock saturated with formation water. In fact, resistivity measurements made in boreholes (well logs) are routinely used to identify and evaluate hydrocarbon-bearing intervals around the borehole. One might expect that CSEM-derived resistivities could be used in a similar manner.
This expectation, however, has not been met for two reasons. First, CSEM data are sensitive to resistivities averaged over a large subsurface volume that may contain regions of both reservoir and non-reservoir rock. Consequently, CSEM methods are not usually able to unambiguously detect individual hydrocarbon-bearing intervals. Second, resistivity is affected by rock properties other than hydrocarbon saturation. In well log analysis, multiple types of logs are commonly available to provide values for these additional rock properties. Such information is not usually available for CSEM surveys, which are commonly acquired over exploration prospects that have not yet been drilled. Any method for quantitative interpretation of CSEM-derived resistivities must address these two issues.
Conventional interpretation of CSEM surveys is directed towards identifying regions of the subsurface that have anomalously high resistivity. Interpretation may involve simply comparing the fields recorded at each receiver to synthetic data computed from a “background” model of subsurface resistivity, or to data recorded on a “reference” receiver that is not expected to be near an anomalous resistivity. The most advanced interpretation methods perform inversion on the recorded fields (e.g., Commer and Newman [1]), and produce a 3D representation of subsurface resistivity that explains the recorded data. Locations where the CSEM data indicates that the resistivity is higher than in the surrounding rock are considered to be potential hydrocarbon reservoirs.
The relationship between CSEM-derived resistivity and hydrocarbon presence is, however, inherently ambiguous. The presence of high resistivity in the subsurface does not guarantee the presence of a hydrocarbon-bearing reservoir. For example, rocks that have very low porosity will have high resistivity even if they do not contain hydrocarbons. And, it is uncertain how high the subsurface resistivity should be before being designated as anomalous. Hydrocarbon-bearing rocks that also contain a significant amount of formation water may not have very high resistivity, and the low vertical resolution of CSEM means that CSEM-derived resistivities may be a mixture of high-resistivity reservoir and interbedded low resistivity non-reservoir rock. Such cases will be missed if the threshold for considering a resistivity to be “anomalous” is set too high.
Additional information, usually geologic or seismic, may be used to reduce this inherent ambiguity. For example, volcanic rocks can have very high resistivities, but if geologic information indicates that volcanics are not present in the survey area they can be eliminated as a possible cause of high resistivity. Seismic data that covers the area of the CSEM survey are normally available. Seismic amplitudes depend on some of the same rock properties as resistivity, so the seismic amplitude response can be combined with the CSEM-derived resistivities to reduce the uncertainty in the interpretation. Incorporating multiple types of information may reduce ambiguity, but normally does not eliminate it. In realistic cases, there is usually more than one possible explanation for the observed CSEM, seismic, and geologic information.
Summarizing, conventional CSEM interpretation does not make quantitative statements about potential hydrocarbon reservoirs because:
1. low vertical resolution means that CSEM-derived resistivities are not representative of individual reservoir intervals;
2. resistivity depends on many rock properties that are poorly known in exploration situations; and
3. the observed data does not uniquely constrain the potential reservoir, even if seismic amplitude information is included.
The current invention addresses all three of these problems.